Pattern classification methods and systems classify input data as belonging to one of several classes based on a statistical match between the data and known distributions of the classes. In many classification applications, such as speaker identification and face recognition, the application may also be required to detect that some input data does not match any of the known classes. This “lack of match” is typically referred to as a rejection.
One approach compares the measured probability of the data, as given by the distributions of all known classes, against a threshold and determines that any data with a probability less than the threshold are rejected, i.e., the data do not belong to any of the known classes.
Another approach represents all data that do not belong to any of the known classes as a class with a distribution of its own, i.e., this class is other. In the art of statistics and classifiers, the other class is generally referred to as the garbage class.
Any input data with a probability, as measured by the distribution of the garbage class, that exceeds a threshold is rejected. For example, distributions for audio signals of speech for speakers Si, i=1, . . . , N are Pi(X). Given a recording X of speech from an unobserved speaker different than any of the speakers S1 . . . SN, the method determines the probability of the garbage class PN+1(X), and determines from its value that the speaker is indeed unobserved.
Often, no training data are available to represent the garbage class. In the example application of speaker identification, if there are data belonging to speaker SN+1 in the garbage class, then one can train the classifier for the distribution for that speaker. However, one is still left with the problem that additional other speakers remain unobserved. So, one must now determine a probability distribution for the totality of all unobserved speakers in the garbage class and not represented by the training data set, in order to determine the distribution of unobserved speakers in the garbage class.